From currencies that resemble digital cash, to utility tokens that act as a fuel for blockchain ecosystems or security tokens, that represent shares in equity, the cryptocurrency market has seen several innovations since its inception. A relatively new addition to these innovations are fan tokens, which give fans the chance to interact with sports clubs, depending on which and how many tokens they are holding. These fan tokens are issued by Socios.com, whose app has been live since October 2019. In this article, I will take a closer look at blockchain statistics of fan tokens as well as blockchain statistics of Socios.com’s native utility token Chiliz. The data snapshot for this analysis was taken on May 31, 2020.

Chiliz Holders

Let’s start with the allocation of Socios’ utility token Chiliz among holders. Since CHZ is issued on both Ethereum and Binance Chain, we have to combine numbers of both chains to calculate the total amount of addresses holding CHZ. Currently, there are 206 addresses holding 1 million Chiliz or more, while 1,460 addresses are holding a minimum amount of 100,000 CHZ. 6,243 addresses are holding at least 10,000 CHZ, whereas at least 1,000 CHZ are held by 7,955 thousand addresses.

1,000 CHZ7,95510,000 CHZ6,243100,000 CHZ1,4601,000,000 CHZ206ADDRESSES WITH AN AMOUNT OF AT LEAST:HOLDERS BY BALANCECHILIZ

Chiliz Transaction Growth

The number of holders (especially with smaller balances) should increase, the more utility CHZ provides. The transaction growth of CHZ on Ethereum already displays the growing utilization of Chiliz. While there were just around one thousand transactions in October 2019, the transaction numbers went up once the Socios.com app went live in late October 2019. November 2019 already saw an 80% increase in transactions with 1,857 transactions compared to October. The transaction numbers then declined in December 2019 (1,492 transactions) and January 2020 (1,256 transactions).

However, February 2020 saw a significant rise in transactions with a 400% increase compared to January 2020. This surge most likely happened due to several different fan token offerings going live on Socios.com, that could only be purchased with CHZ. March 2020 confirmed that growth, with over 6 thousand transactions for the second month in a row. The transaction numbers went even higher in April 2020 with 7,820 transactions of CHZ on Ethereum. A significant surge happened in May 2020, when transaction numbers went up by 89% compared to April to a peak of 14,755 transactions in a single month.

Dec.2019Jan.2020Feb.2020Mar.2020Apr.2020May20201,4921,2566,0336,4107,82014,755TRANSACTIONS OF CHZ ON ETHEREUM PER MONTH:TRANSACTIONS OVER TIMECHILIZ

Fan Token Transaction Growth

Moving on to the transaction growth of fan tokens, we see a slightly different picture. In the first full month after the app release, the number of monthly fan token transactions already reached a peak of nearly 19 thousand transactions in November 2019. The following two months saw transaction numbers around the 15k mark, with 16 thousand transactions in December 2019 and 14 thousand transactions in January 2020. In February 2020, the number of fan token transactions declined to around 4 thousand transactions. March 2020 saw 3.3 thousand transactions, while transaction numbers declined further in April 2020 to 1.6 thousand transactions. May 2020 ended the trend of declining monthly transaction numbers and saw a 418% increase in fan token transactions compared to April 2020 with a 4-months-high of 8,109 transactions.

Fan tokens of seven clubs were distributed in this time span. Six football teams accounted for 93% of all fan token transactions, while one e-sports team caused 7% of all transactions. By far the biggest amount of fan token transactions were fan tokens of Juventus, they accounted for 60% of all fan token transactions. Paris Saint-Germain can be found in the second spot with 11% of all fan token transactions. Galatasaray is ranked third with 9% of all fan token transactions. The fan token transaction numbers of OG, Atlético de Madrid and AS Roma are around the same mark with each accounting for 6-7% of all fan token transactions. FC Barcelona is the most recently launched fan token and although it has only been available in a few hour long token hunt so far, it has already accounted for 1% of all fan token transactions.

Obviously, the number of fan token transactions is highly dependent on the availability of different features in the Socios app. In the last eight months, fan token transactions have been caused by either the token hunt, fan token offerings or interactions with the fan token exchange chiliz.net. The “token hunt” is a feature, in which users can catch fan tokens in an augmented reality setup while fan token offerings allow users to buy fan tokens in return for CHZ. Due to the nature of the token hunt, in which you can only catch one fan token at a time, it causes one transaction per each single token transfer. Fan token offerings and interactions with chiliz.net however, can distribute multiple fan tokens with one transaction. Therefore, the fan token hunt causes way more transactions than fan token offerings and deposits/withdrawals from the exchange, which the data also displays: The fan token hunt wasn’t that often available in the Socios app from February onwards, than it was from November till January, hence the declining transaction numbers.

Most of these fan token transactions distributed tokens to individual users. Some transactions distributed smaller amounts of just one fan token at a time to users, especially within the token hunt. Other transactions in fan token offerings distributed double-digit or even triple-digit amounts of fan tokens with just one transaction. Looking at the cumulative number of distributed fan tokens over time, we can see that already 54 thousand fan tokens were distributed by the end of November 2019. That number went up to 101 thousand fan tokens by the end of December 2019.

Starting from around 100 thousand distributed fan tokens in 2019, the first three months of 2020 saw a stable increase in fan token distribution of around 25 thousand fan tokens per month. Numbers declined in April 2020 with 8.8 thousand distributed fan tokens. However, May 2020 saw a complete turnaround to this relatively stable distribution with nearly 350 thousand distributed fan tokens in a single month, signaling a distribution number of nearly twice the amount, that was distributed from October 2019 to April 2020.

Opposed to fan token transactions, Juventus is not in the lead when it comes to the share of overall distributed fan tokens per club. 39% of all fan tokens distributed until the end of May were fan tokens of Galatasaray. In the second spot, we can find Juventus with 22% of all fan tokens. Paris Saint-Germain is ranked third with 16% of all fan tokens, followed by OG in the fourth spot with 14% of all fan tokens. The remaining three clubs account for 9% of all fan tokens, with Atlético de Madrid accounting for 4%, AS Roma for 4% as well and FC Barcelona for 1%.

These numbers offer interesting insights. When we analyze the monthly numbers of distributed fan tokens, we can see that around 50 thousand fan tokens were distributed in each November and December 2019, while growth slowed down a bit in January to April 2020. These numbers might be a bit surprising, since the fan token of Juventus was the only fan token available in 2019, while several other fan tokens were launched in the first months of 2020. After all, it shows that the fan token of Juventus was the highest demanded fan token in the first seven months of Socios’ app.

May 2020 changed that picture, however. While Juventus accounted for 63% of all fan tokens distributed until the end of April 2020, May 2020 saw an increasing demand for other fan tokens. In May 2020 alone, 191 thousand fan tokens of Galatasaray were distributed, which is significantly higher than the 115 thousand Juventus fan tokens distributed in the previous months from October to April. Other fan tokens were also bought in a much higher frequency in May 2020 with for example 69 thousand PSG fan tokens (total of 20 thousand in prior months) or 55 thousand OG fan tokens (total of 19 thousand in prior months).

Comparing distribution numbers to transaction numbers, we can see clear discrepancies between tokens that have had a higher distribution via the token hunt versus tokens that were highly demanded in fan token offerings. The fan tokens of Galatasaray accounted for just 9% of all fan token transactions, although they represent 39% of all distributed fan tokens. It outlines, that Galatasaray had the highest rate of distributed fan tokens per transaction among all clubs with a ratio of 33.9. The fan tokens of FC Barcelona, that have been only distributed via the token hunt so far, have a ratio of one distributed fan token per transaction.

Considering the fan token exchange chiliz.net, which launched the trading of the fan tokens of Juventus in April 2020, its transaction numbers and token movements have been more difficult to analyze, since multiple wallets interact with the exchange. However, the exchange-related transaction numbers of JUV should amount to around 1,000 transactions since the launch of trading. There were also three transfers with huge amounts of JUV, with a transfer of one million JUV in April 2020 and two transfers with each 100 thousand JUV in May 2020. I suspect all of those tokens to still belong to Socios, since they are most likely used to provide liquidity on chiliz.net but haven’t been sold yet, which is also the reason why I didn’t add them to the number of distributed fan tokens of Juventus.

Wrap-up

In this article, we discussed transaction and distribution numbers of Socios.com’s utility token CHZ alongside their issued fan tokens. Currently, there are 6.2 thousand addresses holding ten thousand CHZ or more. The monthly transaction numbers for CHZ on Ethereum went up 400% in February as soon as more fan token offerings were available in the Socios app. With the token hunt not being available so frequently in 2020 than in 2019, the growth of fan token transactions has slowed down from February 2020 to April 2020, although it saw a significant increase in May 2020. The number of overall distributed fan tokens has been growing relatively stable with around 50 thousand fan tokens distributed in each November and December 2019 and 25 thousand distributed fan tokens in each month from January to March 2020, until distribution numbers exploded in May 2020 with nearly 350 thousand distributed fan tokens. Considering numbers per club, the fan tokens of Galatasaray have been the highest demanded thus far with a share of 39% of all distributed fan tokens. All in all, it is really interesting to see the growth of the Socios ecosystem and it will be exciting to watch, how the numbers discussed in this article will develop over the upcoming months.

Constant growth – That’s how you could label the economic development of the first and second German Bundesliga. Season 18-19, that ended last summer, had a total revenue of 4.8 billion Euro and was the 15th season in a row with growth in total revenue figures. For comparison: In season 03-04, the total revenue of both leagues was at 1.28 billion Euro, which signals a growth of 275% ever since.1 The total revenue figures of both leagues are thereby composed of the revenue figures of each club.2

As transparent as the DFL (German Football League) is about the total revenue figures of both leagues, it can be quite challenging to find revenue figures for each club, especially if you want to see a development over time, since these figures are usually published across many different sources. Therefore, I want to discuss revenue figures of the top 25 German clubs by highest revenue in season 17-18 in this article and also display, how their revenue has developed over the past six seasons.

TOP 3: Bayern, Dortmund and Schalke

At the top of the ranking, we can find the club, that has won every single Bundesliga trophy from season 12-13 to season 17-18. Bayern Munich has not only won the league several times in a row but has also generated the highest revenue in each of the past six seasons. Starting from 433 million Euro in 12-13, the club could grow its revenue in each of the past four seasons and has gathered a revenue as high as 657 million Euro in season 17-18, which is more than the combined revenue of all second Bundesliga clubs in 17-18. For comparison: Bayern earned 35 million Euro in season 19/20 alone from its shirt sponsor Telekom, which is more than most second Bundesliga clubs earn as total revenue in one season.

Borussia Dortmund comes in second with a revenue of 498 million Euro in season 17-18.4 Similar to Bayern Munich, which could increase its revenue by 224 million Euro since 12-13, Dortmund could also increase its revenue by 223 million Euro compared to season 12-13. Although since Dortmund’s revenue was lower than Bayern’s in 12-13, it shows that Borussia could grow its revenue by a larger percentage. A determining factor to this rise in revenue is the club’s tremendous success on the transfer market, where it earned over 100 million Euro in 16-17 and over 250 million Euro in 17-18. Borussia Dortmund is also the only club in the Bundesliga whose shares are publicly traded, with a current market capitalization of close to 600 million Euro.

Schalke 04 is placed third in the ranking with a revenue of 350 million Euro in the year 2018.5 The club from Gelsenkirchen doesn’t publish its revenue figures per season but per year, so we have to compare Schalke’s yearly revenue figures to other clubs’ seasonal revenue numbers. Schalke’s revenue grew from 207 million Euro in 2013 to 264 million Euro in 2015. However, with no participation in the UEFA Champions League from season 15-16 to season 17-18, the next two years saw a decline in revenue growth, with 265 million Euro in 2016 and a decrease to 240 million Euro in 2017. Nonetheless, among other contributing factors the second rank at the end of Bundesliga season 17-18 helped the revenue of the club surge to a high of 350 million Euro in 2018.

On spot 4-6 we can find three clubs, which are each closely connected to well-known companies that are generating billions in revenue each year. Bayer 04 Leverkusen is a former works team of Bayer and the company remains to be the club’s only shareholder. VfL Wolfsburg was not founded as a company team of Volkswagen, but has been closely connected to VW throughout its existence, which also led to the company becoming the only shareholder of the club today. RB Leipzig is also very closely connected to Red Bull, although it is legally not 100% owned by the company.

The impact of these companies towards the clubs can clearly be seen in the revenue figures, since all three of them are among the highest revenue-generating football clubs in Germany. However, RB Leipzig is the only club of the three, that has published its annual revenue figures since 2015.6 From season 18-19 onwards, the clubs seems to have switched to seasonal revenue figures though, which leaves us to estimate the revenue for season 17-18. Leverkusen and Wolfsburg do not disclose their financial data, which makes it necessary to estimate their revenue figures.

In an effort to increase financial transparency, the DFL decided to publish key financial figures for each of the 36 clubs in first and second Bundesliga starting from season 17-18 onwards. These numbers contain revenue figures minus material costs, which can be used to estimate revenue figures of the three clubs quite well, since material costs for football clubs are usually not that high. For previous seasons however, we have to use total revenue numbers of the league as well as revenue figures of specific clusters within the league, which are both published by the DFL. We can then subtract known revenue figures of all other clubs from these numbers to get an estimate of Leverkusen’s and Wolfsburg’s historical revenue. It is a rough estimate but it is as precise as we can get.

The resulting numbers offer interesting insights. Leipzig’s revenue has grown every year and its movement from 4th domestic league to Champions League in just 6 years has put the club on the 5th spot of revenue-generating football clubs in Germany in 17-18. Compared to 2015, RB grew its revenue by 200% from 82 million to an estimated number of 247 million Euro in season 17-18. In contrast to Leipzig, Leverkusen and Wolfsburg have been not only among the highest revenue generating football clubs in Germany in 17-18, their estimated revenue figures show that they have been among Europe’s top revenue generating football clubs for several seasons.

Leverkusen’s estimated revenue numbers have been in an uptrend over the past six seasons. Starting with 100 million Euro in season 12-13, the club could increase its revenue significantly in the following seasons due to continuous Champions League participations from 13-14 to 16-17 and some profitable transfer windows. Compared to season 12-13, Bayer 04 doubled its estimated revenue to 200 million Euro in season 14-15. A temporary peak was reached in 15-16 with 250 million Euro. Growth in revenue slowed down a bit in the following seasons with a minor decline to 225 million Euro in 16-17 and a repetition of the 15-16 peak with 250 million Euro in 17-18.

Wolfsburg’s estimated revenue has been even higher than Leverkusen’s throughout the past seasons and has stayed above 200 million Euro in each season from 12-13 to 17-18. The club’s average estimated revenue over these seasons has been the 4th highest in Germany and is only a few million Euro away from Schalke’s average revenue. Looking at the development over the past few seasons, Wolfsburg’s estimated revenue has been rather constant though, with an estimated revenue of around 200-225 million Euro in four of the six considered seasons. A peak was reached in season 15-16 with a revenue of 350 million Euro due to a Champions League participation and significant earnings on the transfer market.

On spots 7-10 we can find four clubs, whose revenue figures have been around the same level. From Eintracht Frankfurt’s 176 million Euro revenue in 20187 to Hoffenheim’s 165 million Euro revenue in 17-18,10 there is just a difference of 11 million between spot number 7 and 10. Rhenish rivals Borussia Mönchengladbach and FC Cologne fit right in with 172 million Euro revenue each.8,9 As seen with Schalke before, the numbers of the clubs are not exactly comparable, since Frankfurt and Mönchengladbach report only annual figures, whereas the figures of Cologne and Hoffenheim are seasonal.

The numbers for Frankfurt and Cologne outline constant growth. For six years/seasons in a row, their revenue has gone up in every single year. Compared to 2013, Eintracht Frankfurt has grown its revenue by 103%, whereas Cologne even had a growth of 183% compared to season 12-13. Gladbach’s revenue figures have grown each year from 2013 to 2016, although the club’s numbers have been moving in a range from 160 to 200 million Euro since 2015. Hoffenheim’s revenue was also ranging around 70 million Euro from season 12-13 to 14-15, while the revenue increased to 129 million Euro in season 15-16, mainly due to selling Roberto Firmino for 41 million Euro to Liverpool. Further transfers and qualifications for European football competitions led to a revenue of 112 million Euro in season 16-17 and 165 million Euro in 17-18.

Placed on rank number 11 and 12, we have the first two clubs, that are currently playing in the second Bundesliga. In season 17-18, the Hamburger SV has generated 165 million Euro in revenue,11 whereas VfB Stuttgart has earned a revenue of 154 million Euro.12 Both clubs have been among the highest revenue-generating clubs in the past 6 seasons, their average revenue per season over this time span ranks them 7th and 8th among all German football clubs. Both clubs have also underperformed compared to the league in general: While the total revenue of first and second Bundesliga grew by 71% in the past six seasons, HSV’s revenue grew by 25% and Stuttgart’s by 34%.

On the other hand, Hertha BSC Berlin, which is placed thirteenth in the ranking, has outperformed the total league numbers. Having a revenue of 56 million Euro in season 12-13, the club increased its revenue in each of the past six seasons. Hertha closed season 17-18 with a revenue of 146 million Euro, outlining a growth of 159% compared to 12-13.13 Werder Bremen, which is placed on spot number 14, has shown growing revenue numbers from season-to-season as well (with the exception of season 17-18). However, its growth was smaller than the league average with 26% from 12-13 to 17-18.14

FSV Mainz 05 is placed on spot number fifteen with a revenue of 114 million Euro in season 17-18.15 The club’s revenue figures show the same pattern of those of the aforementioned TSG Hoffenheim, with a stable revenue from 12-13 to 14-15 and then a sudden increase due to higher returns in the transfer window. From 12-13 to 14-15, Mainz’ revenue was around 75 million Euro, but then increased to the mark of around 110 million Euro with earnings of over 25 million Euro on the transfer market during each of the past three seasons.

On spots 16 to 20 we can find several clubs, which have been playing at least one season in the second Bundesliga or have mostly reached lower places in the first Bundesliga. SC Freiburg reached spot number 16 in the ranking with a revenue of 100 million Euro in season 17-18. The club’s revenue has been stagnant in the five seasons prior to 17-18, with around 50 million Euro in 12-13, around 75 million Euro in 13-14 to 14-15 and around 55 million Euro in both its sole season in the second Bundesliga (15-16) and the follow-up season in the first Bundesliga (16-17).16

Augsburg is the only club outside of the top 15 in this ranking, that has been playing in the first Bundesliga in each of the past six seasons. With 92 million Euro in revenue in season 17-18, the club is ranked on spot number 17.17 Augsburg’s revenue growth is above the Bundesliga average with a 118% higher revenue compared to season 12-13. Similar to Hoffenheim and Mainz, its revenue surged in 15-16 and has been around the same mark ever since, although contrary to the aforementioned two, those returns didn’t come from the transfer market.

Hannover 96, placed on spot number 18 with a revenue of 88 million Euro in 17-18, is one of the few clubs which had a stable revenue over the past six seasons. With the exception of the club’s second Bundesliga stay in season 16-17, the club’s revenue has been around the mark of 80 million Euro.18 On the contrary, Ingolstadt, which is placed 19th with a revenue of 66 million Euro in 17-18, has been growing its revenue by 177% compared to season 12-13.19 Ingolstadt’s revenue increased by 100% in season 15-16 alone, when the club participated in the first Bundesliga for the first time in the club’s history. However, the club has been relegated to the third league since.

On spot number 20, we can find the only club in the ranking, that has been playing solely in the second Bundesliga throughout the considered time span. FC St. Pauli reached the 20th spot with a revenue of 49 million Euro in season 17-18.20 With a growth of 64% compared to its revenue of 30 million Euro in season 12-13, the club increased its numbers by nearly the same percentage than the league on average.

The five remaining spots in the ranking are filled with two clubs, that are currently playing in the Bundesliga, one club that is currently active in the second Bundesliga and two clubs, that have been relegated to the 3. Liga at the end of season 17-18. 1. FC Nürnberg is placed on rank 21 with a revenue of 44 million Euro in 17-18.21 It is one of just two clubs in the ranking, whose revenue number is lower in 17-18 than in 12-13. Compared to 12-13, Nuremberg’s revenue went down by 18% from 54 million Euro, compared to season 13-14, it went down by 31% from 64 million Euro.

In contrast to FCN, Union Berlin’s revenue figures have been in an uptrend throughout the last six seasons. Starting from 20 million Euro in season 12-13, the club managed to grow its revenue to around 27 million Euro in season 13-14 and season 14-15. In season 15-16, Union reached a revenue figure of 31 million Euro, which grew to 39 million Euro in 16-17. The club’s revenue of 44 million Euro in season 17-18 puts them on place 22 in this ranking.22

Fortuna Düsseldorf is the second club in the ranking whose revenue figures have been lower in season 17-18 than in season 12-13. The club managed to reach a revenue of 51 million Euro in season 12-13, which decreased by 22% to 40 million Euro in season 17-18. It is also one of the few clubs in the ranking, that didn’t continuously publish its revenue figures, most of the figures listed in the graphic below are from newspaper releases rather than from Fortuna’s official website. The number of 30 million Euro for season 14-15 is estimated.23

Eintracht Braunschweig, ranked on spot number 24 with a revenue of 38 million Euro in season 17-18, has growing revenue figures in each season from 14-15 to 17-18, although the club’s revenue in season 17-18 is around the same number than Eintracht’s last Bundesliga season in 13-14.24 FC Kaiserslautern is placed on spot number 25 with a revenue of 36 million Euro in season 17-18. FCK’s revenue has been stable throughout the past seasons with a number of 38-39 million Euro in each of the previous four seasons from 13-14 to 16-17.25

The ranking showed some interesting insights. Among 17 clubs that are currently active in the first Bundesliga, just five clubs from the current second Bundesliga entered the ranking. It outlines the huge monetary difference between first and second Bundesliga, which is also underlined by Bayern Munich’s revenue in season 17-18 being higher than the combined revenue of all second Bundesliga clubs in the same season. Nonetheless, the difference between second and third domestic league seems smaller, since three clubs can be found in the ranking, which have been among the top 25 revenue-generating football clubs in 17-18, but have been relegated to the 3. Liga since.

However, this ranking should not only discuss revenue numbers of German football clubs but also enable a discussion in the first place, since these revenue figures are published across many different sources, making a comparison between clubs and a clubs’ number across different seasons difficult. Even a widely quoted publication like the “Deloitte Football Money League” doesn’t seem to consider all of the numbers mentioned in the ranking, since Wolfsburg’s and Leverkusen’s estimated revenue figures put them among the top revenue generating clubs in Europe throughout the past five seasons, although they were never included in Deloitte’s ranking.

All in all, this research piece should provide more clarity and transparency towards the revenue figures of the top German football clubs over the past six seasons and should therefore encourage discussions on that matter.

From season 17-18 onwards, the DFL decided to publish key financial figures for each club, which also contain revenue figures minus material costs (“Rohergebnis”). The combined revenue figures minus material costs for all 36 clubs are at 4.28 billion Euro, while the total revenue number for both leagues according to the DFL in season 17-18 was at 4.42 billion Euro.

DFL; Finanzkennzahlen: Clubs der Bundesliga in der Saison 2019-20

Sources – Revenue Per Club

3

Data was taken from a news release on Bayern’s official website, that contained revenue figures for the last ten years. The figures are those of “FC BAYERN MÜNCHEN AG Konzern”

FC Bayern München

4

Revenue figures are published in the Bundesanzeiger under “Borussia Dortmund GmbH & Co.” in yearly publications titled “Jahres- und Konzernabschluss zum Geschäftsjahr”. The figures are those of “Borussia Dortmund KGaA (HGB)”.

Borussia Dortmund

5

Schalke publishes its revenue numbers in yearly reports titled “Konzernabschluss” on its website.

FC Schalke 04

6

Revenue figures are published in the Bundesanzeiger under “RasenBallsport Leipzig GmbH” in yearly publications titled “Jahresabschluss zum Geschäftsjahr”.

RB Leipzig

7

Revenue figures are published in the Bundesanzeiger under “Eintracht Frankfurt Fußball Aktiengesellschaft” in yearly publications titled “Jahresabschluss zum Geschäftsjahr”.

Eintracht Frankfurt

8

Revenue figures are published in the Bundesanzeiger under “Borussia VfL 1900 Mönchengladbach GmbH” in yearly publications titled “Jahresabschluss zum Geschäftsjahr”.

Revenue figures are published in the Bundesanzeiger under “TSG 1899 Hoffenheim Fußball-Spielbetriebs GmbH” in yearly publications titled “Konzernabschluss zum Geschäftsjahr”.

TSG 1899 Hoffenheim

11

Revenue figures are published in the Bundesanzeiger under “HSV Fußball AG” in yearly publications titled “Jahresabschluss zum Geschäftsjahr” from season 15-16 onwards. From season 13-14 to season 14-15 revenue figures were published in the Bundesanzeiger under “HSV Fußball AG” in yearly publications titled “Konzernabschluss zum Geschäftsjahr”. The figures for season 12-13 were published in the Bundesanzeiger under “HSV e. V. Konzern” in yearly publications titled “Konzernabschluss zum Geschäftsjahr”.

Hamburger SV

12

Revenue figures are published in the Bundesanzeiger under “VfB Stuttgart 1893 AG” in yearly publications titled “Konzernabschluss zum Geschäftsjahr” from year 2017 onwards. Revenue figures for previous years were extracted from reports about yearly members’ meetings (“Mitgliederversammlung”), that were published on the club’s official website.

Revenue figures were extracted from reports about yearly members’ meetings (“Mitgliederversammlung”), that were published on the club’s official website.

1. FSV Mainz 05

16

Revenue figures were extracted from reports about yearly members’ meetings (“Mitgliederversammlung”), that were published on the club’s official website. For season 12-13, the club didn’t mention an exact revenue figure, Freiburg just stated that it was above the 50-million mark.

Revenue figures are published in the Bundesanzeiger under “Hannover 96 GmbH & Co.KGaA” in yearly publications titled “Jahresabschluss zum Geschäftsjahr”.

Hannover 96

19

Revenue figures are published in the Bundesanzeiger under “FC Ingolstadt 04 Fussball GmbH” in yearly publications titled “Jahresabschluss zum Geschäftsjahr”.

FC Ingolstadt 04

20

Revenue figures were published across different news articles on the club’s official website.

FC St. Pauli

21

Revenue figures were published in different news articles about the balance sheet press conference (“Bilanz-Pressekonferenz”) on the club’s official website.

1. FC Nürnberg

22

Revenue figures were extracted from reports about yearly members’ meetings (“Mitgliederversammlung”), that were published on the club’s official website.

1. FC Union Berlin

23

Revenue figures for season 17-18 were extracted from a report about the yearly members’ meeting (“Mitgliederversammlung”), that was published on the club’s official website. Revenue figures for prior years were taken from newspaper articles. For season 14-15, there was no source available, so that number is estimated.

Revenue figures for season 17-18 were extracted from a newspaper report about the yearly members’ meeting (“Mitgliederversammlung”). Revenue figures for 15-16 and 16-17 were taken from a publication on the club’s official website about the balance sheet (“Bilanz und GuV”). Revenue figures for season 12-13 to 14-15 were extracted from reports about yearly members’ meeting (“Mitgliederversammlung”), that were published on the club’s official website.

There are currently 5,484 cryptocurrencies listed on CoinMarketCap but with Bitcoin, just one cryptocurrency is accountable for over 50% of the total crypto market cap. With so much dominance of the whole market cap and Bitcoin also being a trading pair for most coins, the whole Altcoin market is highly depending on the largest cryptocurrency. Therefore, crypto cycles are mostly divided in Bitcoin bull cycles, where most Altcoins lose value against Bitcoin, or Altseasons, in which most Altcoins gain value against Bitcoin. In this article, I want to discuss and analyze both types of seasons.

Identifying Bitcoin and Altcoin seasons

First of all, we have to identify Bitcoin seasons and Altseasons. While there are certainly single days, in which Altcoins gain more than Bitcoin and vice versa, I prefer to identify these cycles in a macro perspective, allocating at least a few weeks to each season. The seasons can be identified best with the Bitcoin dominance chart provided by CoinMarketCap. When there is a period of rising Bitcoin dominance, it is a Bitcoin season, whereas a period with decreasing Bitcoin dominance signals an Altseason. Utilizing this method, I identified 8 Bitcoin seasons and 7 Altseasons since December 2014 – which are depicted in the following graphic. It also shows the BTC and ALTS dominance simplified from highest/lowest point at the start of a season to the lowest/highest point at the end of a season:

The graphic displays Bitcoin dominance (colored in red) and Altcoin dominance (colored in blue) of the total crypto market capitalization. The most obvious observations are: Bitcoin and Altcoin seasons are alternating patterns and Bitcoin’s dominance has decreased from 2016 to 2017, until it started to rise from 2018 onwards. While Bitcoin’s dominance was above 80% throughout 2015 and 2016, it went as low as 33% in January 2018. Since then, it has gone up to a value as high as 71% recently and is currently at 67%.

Length and frequency of seasons

Let’s have a closer look at the length and frequency of the seasons. On average, a Bitcoin season lasted 166 days, while an Altcoin season lasted 103 days. Considering particular months, the distribution of seasons is relatively balanced. Outliers in this dataset are the months May, July and October. In four of the last five years Altcoins gained against Bitcoin in May, while Bitcoin’s market cap rose higher percentage-wise against Altcoins in July and October. See the following graphic for more details:

How did the market cap of Bitcoin and Altcoins change during their respective seasons? The average Bitcoin season made Bitcoin’s market cap increase by 125%, while the average Altcoin season made the overall market cap of all Altcoins increase by 575%. However, these averages are largely inflated by the price movement in 2017, where the Altcoin market cap increased by 3296% in the first half of the year, while the Bitcoin market cap increased by 605% in the second half of the year.

If we erase these two seasons from the average calculation, Bitcoin’s market cap increased by an adjusted average of 45% in a Bitcoin season while the Altcoin market cap increased by an adjusted average of 122% in an Altcoin season (since December 2014). Therefore, the Altcoin increases are nearly 3x higher percentage-wise in an Altcoin season than Bitcoin increases in a Bitcoin season. However, the performance of Altcoins in Bitcoin seasons is also worse than the performance of Bitcoin in Altcoin seasons. On average, the market cap of Altcoins decreased by 16% during Bitcoin seasons, whereas the Bitcoin market cap increased by 25% on average in Altcoin seasons. The following graphic contains all identified seasons since December 2014 and their respective market cap decreases and increases. I also added the current Bitcoin season, that started in February 2020, although it is not included in the above-mentioned calculations yet:

Criticism concerning this analysis can be targeted towards the rather small sample size. Bitcoin has been around for only 11 years and the Altcoin market as a whole is even younger. At the end of 2013, there weren’t even 100 Altcoins listed on CoinMarketCap, which is why I decided to start with this analysis at the end of 2014, when there were at least 500 Altcoins listed on CoinMarketCap. This provides us with five years of market data, that resulted in the identification of 15 different seasons, which is a small sample size. It’s not a failure of this analysis though, it is simply a result of the crypto market being relatively new. It will be interesting to see, whether the same patterns will be visible in several overhauls of this analysis during the next few years.

Wrap-up

Bitcoin and Altcoin seasons are alternating market movements in the crypto market. In this analysis, a prolonged increase of Bitcoin dominance in the whole crypto market capitalization was declared as a Bitcoin season, while a prolonged increase of Altcoin dominance was declared as an Altcoin season. This method identified 8 Bitcoin and 7 Altcoin seasons. A comparison of these seasons against each other outlined, that Bitcoin dominance decreased over time, even though Bitcoin seasons are on average 1.6 times longer than Altseasons. During the last 5 years, Altcoin seasons were more likely to happen in the first half of a year than in the second half. The analysis also displays that Altcoins on average increased by a larger percentage in an Altcoin season than Bitcoin increased in a Bitcoin season. However, the Altcoins’ USD market capitalization on average decreased during Bitcoin seasons, whereas Bitcoin’s market cap in fact increased on average during Altcoin seasons.

Among many different indicators, that can be applied to describe and predict the price of Bitcoin and other cryptocurrencies, the “Relative Strength Index” (RSI) is one of the most popular indicators. It displays the price action in a fixed range and offers a clear classification, of whether the price of an asset is in oversold or overbought range. However, as with all indicators it is questionable, if the shown data can really be used for price forecasts or if the alleged impacts cannot be proven statistically. Therefore, I will take a look at the RSI indicator in this article and analyze its predictive impact on Bitcoin’s daily price.

What is the RSI?

Let’s start with a description of the RSI. The “Relative Strength Index” (RSI) is a momentum indicator used in technical analysis, that measures the magnitude of price movements on a scale of 0-100.1 Its main purpose is to identify whether the price of an asset is in overbought, neutral or oversold range.2 An RSI value below 30 is commonly perceived as oversold, indicating a future uptrend, whereas an RSI value above 70 is perceived as overbought, indicating an upcoming downtrend.3 In general, the lower the RSI value, the higher the chance for the price bouncing upwards and the higher the RSI value, the higher the chance for the price moving downwards. However, it is not guaranteed that the price will move in the direction indicated by the RSI.

Daily RSI values of Bitcoin

So much for the theory, now onto the practical application. As of Spring 2020, there are around 9.7 years of public trading data available for Bitcoin against the U.S. Dollar. On 58.5% of the days in that time span, Bitcoin has had a daily RSI value of above 50, while the daily RSI value has stayed below 50 for 41.5% of the time. Considering overbought and oversold range, Bitcoin has spent way more time in the overbought area, than in the oversold zone. On 18.4% of the days, Bitcoin’s daily RSI was above 70, which is generally perceived as overbought. On 8.4% of the days, its daily RSI was above 80 and on 1.8% of the days it even had an RSI above 90. Considering oversold range, Bitcoin’s daily RSI was on just 4.2% of the days below 30. A daily RSI below 20 was even rarer with just 0.8% of the days, while the daily RSI moved below 10 on just one single day.

Let’s have a closer look at how profitable certain trading strategies were in the past. One simple strategy would have been to buy Bitcoin at the first daily close below a certain RSI value and to sell it again at the first daily close above a certain higher RSI value. I identified five different occasions, where this would have been profitable on average, although the sample sizes are rather small – each pattern occurred between 10 and 30 times in the last 10 years. The five patterns and their average returns are listed in the graphic below:

SOLD AT 1ST DAILY CLOSE ABOVE 70:BOUGHT AT 1ST DAILY CLOSE BELOW 50,7.4%74.831AVERAGE RETURN:AVG. DAYS IN RANGE:NUMBER OF TIMES:SOLD AT 1ST DAILY CLOSE ABOVE 80:BOUGHT AT 1ST DAILY CLOSE BELOW 50,27.2%116.921AVERAGE RETURN:AVG. DAYS IN RANGE:NUMBER OF TIMES:SOLD AT 1ST DAILY CLOSE ABOVE 70:BOUGHT AT 1ST DAILY CLOSE BELOW 30,12.6%71.318AVERAGE RETURN:AVG. DAYS IN RANGE:NUMBER OF TIMES:SOLD AT 1ST DAILY CLOSE ABOVE 50:BOUGHT AT 1ST DAILY CLOSE BELOW 30,6.8%24.423AVERAGE RETURN:AVG. DAYS IN RANGE:NUMBER OF TIMES:SOLD AT 1ST DAILY CLOSE ABOVE 30:BOUGHT AT 1ST DAILY CLOSE BELOW 20,7.8%4.211AVERAGE RETURN:AVG. DAYS IN RANGE:NUMBER OF TIMES:RSI – BITCOINSTRATEGY 1

We can also reverse the first trading strategy and sell Bitcoin at a certain RSI value, to buy it back when it has reached a lower RSI value. However, in contrast to trading strategy number 1, this didn’t play out as expected. If one would have sold Bitcoin at a daily closing RSI value of 70, 80 or even 90, with the goal to buy it back once it has reached a lower RSI value, it would have resulted in a loss compared to the amount of sold Bitcoin. Due to its incredibly bullish price action, Bitcoin has reached very high RSI values in the past, without necessarily slowing down at the first time it reached those values.

Let’s say our goal is to sell at a daily close in the oversold zone above 70 and buy back in the neutral area at 50. With a buy back at the first daily closing RSI below 50, a sell at the first daily closing RSI above 70 would have resulted in a price increase of 95%, while a sell at the first daily closing RSI above 80 would have resulted in an average price increase of 112% and a sell at the first daily closing above 90 would have resulted in a price increase of 119%. As a conclusion, trading strategy 2 on average would have resulted in around 50% less Bitcoin at the point of the buy back compared to the selling point.

Wrap-up

Looking back at the relative strength index during Bitcoin’s last ten years of public trading data, we can see that the RSI has spent way more time in overbought territory than in the oversold range. In addition to that, Bitcoin’s RSI value has stayed above 50 for around 60% of the time. Of the two discussed trading strategies, only the first one had been profitable on average, in which one would have bought Bitcoin at a lower RSI value to sell it again at a higher RSI value. Selling Bitcoin at a high RSI value and buying it back at a lower RSI wouldn’t have worked on average, since one would have only been able to buy back less Bitcoin than one has sold before.

Articles, that may interest you:

Sources

1

“The relative strength index (RSI) is a momentum indicator that measures the magnitude of recent price changes to evaluate overbought or oversold conditions in the price of a stock or other asset. The RSI is displayed as an oscillator (a line graph that moves between two extremes) and can have a reading from 0 to 100.”

“Most oscillators including RSI work with so called overbought and oversold areas. Market is overbought when there has been “too much” buying in the recent past (last few price bars). Conversely, an oversold market occurs when sellers have prevailed and pushed the price down. Common way of looking at oscillators and their overbought and oversold areas is to think of them as a signal to trade in the other direction. As the name suggests, when market is overbought, the buying has been excessive and we can expect the price to make a downward correction or a reversal. On the other side an oversold market signals a possible increase in prices.”

“The Relative Strength Index (RSI), developed by J. Welles Wilder, is a momentum oscillator that measures the speed and change of price movements. The RSI oscillates between zero and 100. Traditionally the RSI is considered overbought when above 70 and oversold when below 30. Signals can be generated by looking for divergences and failure swings. RSI can also be used to identify the general trend.”

Bitcoin’s infamous block 1 was mined on January 9, 2009. The network has been live ever since and has produced 618,657 blocks up to this point. It is the longest running blockchain and therefore, I have taken a look at it from a statistical standpoint in this article to conclude, how its key metrics have developed over time.

Transaction Growth

Let’s start with the transaction growth.1 In 2009, there were 32,716 Bitcoin transactions. That number went up by 566% in 2010, with then 185 thousand transactions. 2011 was the first year that saw over 1 million Bitcoin transactions with 1.91 million transactions. The transaction growth continued in 2012, with 8.44 million yearly transactions. The growth rate slowed down a bit from 2013 on, but was still with 19.7 million transaction 2.3 times higher than the year before.

Even in the bear market of 2014, the number of Bitcoin transactions continued to grow – the year saw 25.3 million transactions. Transaction growth accelerated again in 2015, which had with 45.7 million transactions 1.8 times as many as 2014. In 2016, transaction numbers went up to 82.6 million, while Bitcoin broke the mark of 100 million yearly transactions in 2017 with 104.1 million transactions. The bear market of 2018 led for the first time in Bitcoin’s history to a lower number of transactions compared to the year before with “only” 81.4 million transactions. However, 2019 saw a new all-time high for transactions with 119.8 million yearly Bitcoin transactions.

A closer look at the transaction numbers per half-year over time offers additional insights. We can see, that the transaction numbers varied quite a lot between different years, but didn’t change that much if you look at the difference between the first and the second half of a year. The highest transaction number per half-year has happened in the first half of 2019 with 60.9 million transactions, followed by 58.8 million transactions in the second half of 2019. The first half of 2018 saw the lowest number of Bitcoin transactions in the last four years with just 37.6 million transactions. The following chart visualizes the transaction numbers:

2H20161H20172H20171H20182H20181H20192H201944.252.251.837.643.858.860.9MILLION TRANSACTIONS PER 6 MONTHS OVER TIME:20090.03M20100.2M20111.9M20128.4M201319.7M201425.3M201545.7M201682.6M2017104.1M201881.4M2019119.8MTRANSACTIONS OVER TIMEBITCOIN

Daily Active Addresses

The daily active Bitcoin addresses offer similar insights than the transaction numbers.2 The average value of the number of daily active Bitcoin addresses has been increasing every single year from 2009 to 2017. While there was just an average of 92 daily active addresses in 2009 and 382 in 2010, the number exploded in 2011 with 10,276 average daily active addresses. Growth continued in the following years, with an average of 23k daily active addresses in 2012, 68k in 2013 and 154k in 2014. In 2015, the average number of daily active addresses reached the mark of a quarter million for the first time and rose even higher to 408k addresses in 2016. In 2017, the average number of daily active addresses peaked at 565k but wasn’t that far off from the top in the following years, with 468k addresses in 2018 and 529k in 2019.

201320142015201620172018201968k154k251k408k565k529k468kAVERAGE DAILY ACTIVE ADDRESSES PER YEAR OVER TIME:DAILY ACTIVE ADDRESSESBITCOIN

Dormant Bitcoin

However, not only did the number of daily active addresses rise over the past decade, but also the number of dormant Bitcoin.3 Bitcoin are generally perceived as dormant, if they haven’t been moved for a certain time span. The number of Bitcoin, that have been dormant for the longest time span are 1.15 million. Those 1.15 million Bitcoin haven’t been moved for 10 years and are presumably coins that are owned by Satoshi. It is not only Satoshi’s coins haven’t been moved for a longer time span, though. A total of 1.9 million Bitcoin have been dormant for 9 years while 2.4 million Bitcoin haven’t been moved for 8 years.

Those Bitcoin are generally perceived as “lost”. It has been a widely adopted assumption that the owner of an address lost access to the private key, if they haven’t moved the coins for such a long time while Bitcoin’s price went up over 1000x. The number of 4 million Bitcoin, that have been perceived as lost4 is equal to the number of Bitcoin, that haven’t been moved for the last five years (3.96 million). Adding to that, around 28% of all Bitcoin currently in circulation haven’t been moved for the last three years. With 8.26 million Bitcoin, around 45% of all Bitcoin currently in circulation have been dormant for two years, while 13.15 million Bitcoin haven’t been moved for the last year.

Let’s move on from the number of dormant Bitcoin to the current distribution of Bitcoin among holders.5 The number of people who own at least 1 Bitcoin have been called members of the “21 million club”, since only a maximum of 21 million people could ever own one Bitcoin. However, a look at the blockchain shows that there are currently only 703 thousand addresses holding at least 1 Bitcoin, while a total of 2.7 million addresses are currently holding 0.1 Bitcoin and a total of 7.6 million addresses are currently holding 0.01 Bitcoin.

If there are less than a million addresses holding at least one Bitcoin, there need to be other addresses holding a balance of way more than 1 Bitcoin. Currently, there are 144 thousand addresses holding at least 10 Bitcoin and 14.5 thousand addresses holding at least 100 Bitcoin. Some of the biggest Bitcoin wallets belong to cryptocurrency exchanges. Huobi’s cold wallet is currently the biggest Bitcoin address with 256 thousand Bitcoin, followed by Bitfinex’ cold wallet with 177 thousand Bitcoin. Overall, cryptocurrency exchanges could currently store up to 2.5 million Bitcoin.6

0.01 BTC7,636,6330.1 BTC2,749,9901 BTC702,73510 BTC144,212100 BTC14,543ADDRESSES WITH AN AMOUNT OF AT LEAST:HOLDERS BY BALANCEBITCOIN

Wrap-up

In this article, we looked at Bitcoin’s blockchain statistics. Both transaction numbers and the average number of daily active addresses have been increasing in each of Bitcoin’s first 8 years. The yearly number of transactions had its peak in 2019, while the average number of daily active addresses peaked in 2017. Around 4 million Bitcoin haven’t been moved for the last five years, while 1 million Bitcoin have been dormant for the last decade. Currently, there are 702 thousand addresses which own at least 1 Bitcoin, whereas some of the largest Bitcoin addresses are considered to be owned by cryptocurrency exchanges.

Articles, that may interest you:

Sources

1

Information about Bitcoin transactions was taken from blockchain.com. You can find the chart on blockchain.com under “data” -> “charts” -> “Total Number of Transactions”.

Blockchain.com; Total Number of Transactions; 10.02.2020

2

Information about daily active Bitcoin addresses was taken from blockchain.com. You can find the chart on blockchain.com under “data” -> “charts” -> “Unique Addresses”.

Blockchain.com; Total Number of Transactions; 17.02.2020

3

Data about the cumulative sum of dormant Bitcoin can be found on bitinfocharts.com. You can find the chart under “Bitcoin” -> “Dormant” -> “Cumulative sum in dormant Bitcoin addresses”.

Bitinfocharts.com; Dormant Bitcoin; 22.02.2020

4

“According to new research from Chainalysis, a digital forensics firm that studies the bitcoin blockchain, 3.79 million bitcoins are already gone for good based on a high estimate—and 2.78 million based on a low one.”.

Data about Bitcoin holders can be found on btc.com. You can find the chart under “Stats” -> “Address Rich List”.

Btc.com; Address Rich List; 10.02.2020

6

The website chain.info (mostly in Chinese) offers permanently updated data on the amount of Bitcoin held by exchanges. According to them, the biggest crypto exchanges could hold up to 2.5 million Bitcoin.

With a total of over 600 million transactions and 85 million unique addresses, Ethereum is arguably the biggest blockchain currently in existence. The blockchain achieved all these numbers in just 4.5 years – Ethereum’s first block was mined on July 31, 2015. In this article, I will take a look at several statistics of the Ethereum blockchain and outline, how they developed over time. The sources for the underlying data were taken from Etherscan and Ethplorer.

Transaction Growth

The first aspect to look at is transaction growth. Ethereum started out with around 1 million transactions in the second half of 2015. This number rose to 5.5 million transactions in the first half of 2016 and 8.1 million transactions in the second half of 2016. The growth accelerated in 2017, with 19.3 million transactions in the first half of 2017 and 83.7 million transactions in the second half of 2017. The transaction number per half-year peaked in the first half of 2018 with 144.5 million transactions. Since then, the transaction numbers per half-year have stayed above the mark of 100 million transactions with 106.7 million in the second half of 2018, 117.9 million in the first half of 2019 and 125 million transactions in the second half of 2019.

19.3% of those transactions can be attributed to different entities. At least 12.5% of all transactions have been caused by centralized exchanges, although it has to be noted that this number only includes transactions of main exchange addresses and not transfers to individual users’ deposit addresses. The total number of transactions caused by centralized exchanges is therefore likely higher than 12.5%. The number of transactions caused by decentralized exchanges is with 4.3% of all transactions on Ethereum lower than the number of transactions caused by centralized exchanges. However, both decentralized exchanges Etherdelta and IDEX have each caused more transactions than the main exchange addresses of any other centralized exchange, except Binance. Looking at other areas than crypto exchanges, 1.7% of all transactions can be attributed to gaming dapps while 0.8% can be matched to gambling dapps.

2015/20161H20172H20171H20182H20181H20192H201914.719.383.7144.5106.7125.0117.9MILLION TRANSACTIONS PER 6 MONTHS OVER TIME:TRANSACTIONS OVER TIMEETHEREUMGAMING1.7%GAMBLING0.8%DECENTRALIZEDEXCHANGES4.3%CENTRALIZEDEXCHANGES12.5%PERCENTAGE OF TRANSACTIONS CAUSED PER ENTITY:

Address Growth

Alongside transaction growth, it is also interesting to see the numbers of new addresses created over time on the Ethereum blockchain. An address is perceived as ‘new’ on the day it received funds for the first time. The blockchain started with 40 thousand transactions in 2015, the number then went up to 948 thousand unique addresses at the end of 2016. Again, growth accelerated in 2017, with 2.89 million new addresses in the first half of the year and 14.62 million new addresses in the second half. The number of new addresses created in a half-year period peaked in the first half of 2018, with 21.75 million new transactions. Since then, the total number of new addresses has been stable, with around 14.5 million transactions per half-year, which is also the same level we saw in the first half of 2017:

Let’s take a closer look at the assets on Ethereum. To measure the adoption of an asset, I like to consider the amount of holders of at least 100 USD of this particular asset. It is a better metric than the total amount of holders, since it ignores addresses with just tiny amounts of a token, whereas an amount of 100 USD is also small enough to not only focus on whales.

In a ranking of ERC-20 tokens by addresses holding at least an amount of 100 USD worth of that asset, Tether (USDT) is ranked in the first spot. It has by far the most addresses holding a balance worth 100 USD with 186 thousand addresses. On the second spot, we can find Chainlink (LINK), which has 50k addresses holding 100 USD or more. The utility token of the Brave browser, Basic Attention Token (BAT) is ranked third with 44k addresses. On spot number 4 and 5 we can find OMG (38k addresses) and ZRX (28k addresses).

From the sixth spot on, the ranking becomes more compact, since the difference between addresses per tokens is not as huge, as we have seen with the first five tokens. Enjin is placed sixth with a total of 15k addresses holding ENJ worth 100 USD or more. On spot number seven, we can find HOT with 12k addresses, while spots number 8 and 9 go to NEXO (12k addresses) and GNT (11k addresses). NPXS is ranked tenth and is also the only remaining token with over 10k addresses holding an amount of 100 USD or more.

Unfortunately, I couldn’t find a complete rich list for Ethereum’s native coin Ether, since Ethplorer only provides rich lists for Ethereum tokens. The most detailed rich list of Ether holders was provided by Etherscan, however, they only listed the top 10,000 addresses, which all hold more than 750 ETH. Nevertheless, we can gather from that list that 1,211 addresses hold at least 10,000 ETH and 8,132 addresses hold at least 1,000 ETH.

GNT11,417NEXO11,794HOT12,359ENJ15,466ZRX27,521OMG38,338BAT44,201LINK49,589USDT186,431ADDRESSES WITH A BALANCE OVER 100 USD PER ASSET:HOLDERS PER ASSETERC-20

Wrap-up

In this article, we looked at blockchain statistics of Ethereum. Since its launch in mid-2015, both the network’s transaction and address growth have peaked in the first half of 2018. For the last 1.5 years, transactions have been over the mark of 100 million per half-year, while the amount of new addresses created per half-year has been at around 14.5 million. Looking at holders per asset, there are currently 10,000 addresses holding 750 ETH or more. A ranking of holders per asset with a balance of at least 100 USD, Tether (USDT) is ranked in the first spot, followed by Chainlink (LINK) and Basic Attention Token (BAT).

The NEO blockchain has been live since the 4th quarter of 2016, when its first block was issued on October 16. Since then, the blockchain has not only hosted its own cryptocurrency NEO alongside its parallel currency GAS but has also become the home for several NEP-5 tokens, that were issued on the same blockchain. In this article, I want to look at statistics of the NEO blockchain and see, how various metrics have developed over time. As sources, I used the blockchain explorer of NewEconoLabs and the aggregated statistics websites Neodepot and NeoEconomy.

Transaction Growth

Let’s start with the transaction growth. Even though NEO went live in in mid-October 2016, there were already 345 thousand transactions in those last two and a half months of 2016. The number increased to a total of 5.792 million transactions in 2017, with 86% of those transactions coming in the second half of 2017. The first half of 2018 marks NEO’s peak for transactions per half-year: 9.857 million transactions were executed in the first six months of 2018. The second half of 2018 saw 7.764 million transactions, leading to a total of 17.622 million transactions in 2018. The total number of transactions in 2019 was at 8.479 million, with 56% of the transactions coming in the second half of 2019.

As can be expected, deposits and withdrawals from exchanges are causing many of those transactions. Interactions with the main exchange addresses of Binance, Bitfinex, Bittrex, Gate.io, Kucoin and Switcheo have caused a total of 2.72 million transactions up to this point. Furthermore, I would expect Huobi to have caused at least 300k transactions as well – However, they split their NEO holdings over 30+ addresses (potentially even more), which makes it impossible to conclude their total transaction number. Adding to that, the transactions caused by main exchange addresses do not include user deposits to their individual exchange deposit addresses. Therefore, the total number of transactions caused by exchange deposits and withdrawals could very well be around 5 million, which would currently equal 15% of all transactions on the NEO blockchain. The following graphic summarizes these transaction statistics:

2H20161H20172H20171H20182H20181H20192H20190.340.844.969.867.764.753.73MILLION TRANSACTIONS PER 6 MONTHS OVER TIME:TRANSACTIONS OVER TIMENEOBITTREX1.1%BINANCE2.3%KUCOIN1.8%SWITCHEO2.6%PERCENTAGE OF TXNS CAUSED BY EXCHANGE ADDRESSES:

Address Growth

Alongside transaction growth, it is also interesting to see the numbers of new addresses created over time on the NEO blockchain. An address is perceived as ‘new’ on the day it received funds for the first time. In 2016, just 5,179 addresses received funds on the NEO blockchain. The number of newly issued addresses went up to 14,203 in the first half of 2017 before address growth really accelerated in the second half of 2017 to 457,115 new addresses. The number of newly issued addresses then saw a peak in the first half of 2018 with 807,059 addresses. The number has been decreasing ever since with 354,893 new addresses in the second half of 2018, 117,079 new addresses in the first half of 2019 and 87,386 new addresses in the second half of 2019. However, the number of new addresses created in the second half of 2019 is still six times higher than the number of new addresses created in the first half of 2017.

Let’s move on to the number of addresses holding the two most popular assets on the NEO blockchain. Currently, there are 180,040 addresses holding at least 1 NEO while 102,439 addresses are holding at least 1 GAS. A balance of at least 100 NEO is currently held by 29,273 addresses, while a balance of at least 100 GAS is currently held by 6,401 addresses. See the following graphic for more details:

1 NEO180,04010 NEO104,192100 NEO29,2731,000 NEO4,03110,000 NEO441ADDRESSES WITH AN AMOUNT OF AT LEAST:1 GAS102,43910 GAS32,468100 GAS6,4011,000 GAS86810,000 GAS85ADDRESSES WITH AN AMOUNT OF AT LEAST:HOLDERS BY BALANCENEO AND GAS

Holders per NEP-5-Tokens

NEO and GAS are not the only assets issued on NEO’s blockchain. In fact, I found 36 NEP-5 tokens that are traded on crypto exchanges. In an analysis of holders with a balance of over 100 USD per asset, NEX is ranked in the first spot with 8,132 addresses. The ranks 2 – 4 belong to WWB, SWTH and TKY with around 1,800 addresses each. DBC is placed on spot 5 with 1,217 addresses, while CPX and QLC are ranked 6th and 7th with around 900 addresses each. SOUL and TNC are ranked on spot 8 and 9 with around 550 addresses. The other 27 NEP-5 tokens in the data sample have less than 400 addresses holding an amount of over 100 USD per asset.

TNC528SOUL559QLC901CPX949DBC1,217TKY1,709SWTH1,840WWB1,908NEX8,132ADDRESSES WITH A BALANCE OVER 100 USD PER ASSET:HOLDERS PER ASSETNEP-5

Wrap-up

This article discussed various statistics of NEO’s blockchain. With block 1 issued in October 2016, both the number of transactions and the number of newly created addresses have seen a peak in the first half of 2018. Looking at holders per asset, there are currently 180 thousand addresses holding at least 1 NEO. Of 36 publicly traded NEP-5 tokens, five have over 1,000 addresses holding an amount of over 100 USD of that particular asset.

In contrast to many newer projects in the crypto space, the Skycoin blockchain has already been running for several years. All of its transactions can be monitored in Skycoin’s handy blockchain explorer, which also has a feature-rich API. However, the explorer doesn’t contain detailed statistics about the blockchain yet, which is why I decided to create some in-depth statistics and present them to you in this article. The data used for this analysis starts at block 1 (start of Q2 2015) and ends at block 129,032 (end of Q4 2019).

Transaction Growth

Let’s start with the transaction growth over time. Block 1 of Skycoin’s blockchain was issued on April 02, 2015. Nearly five years have passed since then and the transaction volume is steadily growing, though the usage started out rather slow. In 2015, the blockchain registered 262 transactions, which increased to 311 transactions in 2016. That changed in 2017. With increasing public awareness and Skycoin’s first exchange listings, the usage of the network increased during every quarter. In Q2 2017 it was over a thousand transactions for the first time, in Q4 2017 the blockchain registered 4500 transactions. This growth continued in 2018: Q1 had 13k Skycoin transactions, Q2 about 24k transactions, Q3 17k transactions and Q4 2018 an all-time record of 27.6k transactions. The transaction growth slowed down after that, Q1 and Q2 of 2019 each had 13k transactions, while it was just 9k transactions in Q3 and 5k transactions in Q4. Overall, the number of transactions in 2019 have been 6 times higher than in 2017, but have been 50% lower than in 2018.

A great amount of these transactions have been interactions with exchange addresses, such as individual exchange deposit addresses or the exchanges’ withdrawal addresses. In general, 78% of all transactions have been interactions with exchange addresses. Breaking it down to individual exchanges, 59% of all Skycoin transactions have been interactions with Binance and 16% have been interactions with Cryptopia. Just 3% of all Skycoin transactions have been interactions with other exchanges. The recently launched Skycoin-based sports betting service “Moojiebet” already accounts for 1% of all Skycoin transactions. The following graphic summarizes these transaction statistics:

TRANSACTIONS OVER TIMESKYCOIN2015265201631120177,079201882,371201941,689MOOJIEBET1%CRYPTOPIA16%OTHER EXCH.3%BINANCE59%PERCENTAGE OF TRANSACTIONS CAUSED PER ENTITY:

Address Growth

This increase of transactions came alongside an increase in unique addresses, that held a minimum of one Skycoin. At the end of 2015, just 98 addresses held at least one Skycoin. This number increased to 166 addresses at the end of 2016. However, the growth in addresses and therefore the distribution of Skycoin really took off in 2017 after the first exchange listings. In the middle of 2017 the circulating supply of Skycoin was still distributed among 400 addresses, while this number increased to 2,000 addresses at the end of the year. The growth continued during 2018: At the end of Q2 2018 around 7.9k addresses held at least 1 Skycoin, which increased to 10.8k addresses at the end of Q4 2018. As of December 31, 2019 there are 12,054 addresses with at least one Skycoin.

It shows, that Skycoin’s circulating supply is distributed among 30 times as many addresses than it was two and a half years ago and is therefore in an ongoing decentralization process. Furthermore, not only the amount of addresses with at least one Skycoin has been growing but also the amount of addresses with a minimum balance of 100 Skycoin. At the end of 2019, there are over 5,000 addresses with at least 100 Skycoin, which is a 25% growth to one year ago and a 500% increase compared to two years ago. In the graphic below you can see the growth of addresses with at least a balance of 1, 10, 100 and 1000 Skycoin over time:

1433521,6395,8217,9518,7698,812ADDRESSES WITH A MINIMUM OF 10 SKYCOIN OVER TIME1002669622,7834,0425,0635,076ADDRESSES WITH A MINIMUM OF 100 SKYCOIN OVER TIME751844217081,0271,5111,569ADDRESSES WITH A MINIMUM OF 1,000 SKYCOIN OVER TIME2H20161H20172H20171H20182H20181H20192H20191664072,0087,89810,76611,62512,054ADDRESSES WITH A MINIMUM OF 1 SKYCOIN OVER TIMEGROWTH OF ADDRESSESSKYCOIN

Coin Hours over Time

However, Skycoin is not the only asset on Skycoin’s blockchain, it also contains Skycoin’s parallel currency Coin Hours. Coin Hours have both an inflationary and a deflationary side, since every Skycoin generates one Coin Hour per hour (inflationary) but also a certain amount of Coin Hours is burned in each Skycoin transaction (deflationary). In general, the circulating supply of Coin Hours has been growing over time but has also had massive decreases caused by individual transfers that burned a ton of Coin Hours.

At the end of 2015, the circulating supply of Coin Hours was at 6.3 billion while it was at 23.6 billion at the end of 2016. The circulating supply went as high as 29.5 billion on April 6, 2017 until a huge number of 13.5 billion Coin Hours were burned in one transaction: After that, the circulating supply of Coin Hours increased constantly with occasional burns once a “Coin Hour whale” decided to move his Skycoin and therefore burn up to 50% of his Coin Hours. At the end of 2017, there were 27.5 billion Coin Hours in circulation, which increased to 69.7 billion Coin Hours at the end of 2018. At the end of 2019, the circulating supply is at 185 billion Coin Hours.

Looking at these numbers, it may be surprising that a total of 519 billion Coin Hours have already been burned, which is around 2.8x as many Coin Hours as the current circulating supply. The circulating supply of Skycoin (at 17 million SKY at the end of 2019) has only generated 283 billion Coin Hours thus far, however, every time a distribution address with one million SKY is opened, the Coin Hours those one million SKY generated over the past five years can get introduced to the circulating supply. Around 408 billion Coin Hours have been released this way from distribution addresses, though most of them have been burned immediately.

In parallel to the transaction analysis, exchanges are not just responsible for the majority of transactions, they have also burned many Coin Hours over the past three years. A total of 82 billion Coin Hours have been burned in exchange-related transfers, of which 63 billion were burned by Binance, 15 billion by Cryptopia and 4 billion by other exchanges. Check out the following graphic for a visualization of all mentioned Coin Hour statistics:

In this article we discussed several statistics of Skycoin’s blockchain. We saw that Skycoin transactions have been growing over time and reached a peak in Q4 2018. Address growth has been steady as well, with both growing addresses overall and growing addresses with a substantial amount of Skycoin. Looking at Coin Hours, their circulating supply has been growing exponentially over time, even though sudden burns of huge Coin Hour amounts decreased the circulating supply substantially.

There are several tools and technical indicators to analyze price movements in charts. Whether it may be candlestick formations, the relative strength index, moving averages or other approaches, traders are constantly searching for the best method to predict future price movements. However, most of the patterns shown by these indicators are only said to have certain impacts on future price movements without having been statistically proven for cryptocurrencies yet. Therefore, I want to have an in-depth look at a certain candlestick formation in this article and examine, whether its presumed impact can be proven by numbers.

Hammer

The candlestick formation “Hammer” is one of the most widely known candlestick patterns. It is a candlestick with a relatively small body that is accompanied by a long wick to the downside and a short to non-existent wick to the upside. The hammer visualizes an interval in a chart, in which lots of selling pushed the price down initially, before the bulls pushed the price back up, thus positioning the closing price relatively near to the opening price.

Two types of the hammer candle allegedly have a predictive impact on prices. One is a hammer candle that is positioned in a chart after a downtrend. It is assumed to indicate the end of a downtrend and is widely recognized as a bullish reversal pattern. The second hammer pattern is called “hanging man”, which is a hammer that appears after an uptrend and is supposed to be a bearish reversal pattern.

While the human eye might spot hammer candles very easily on a chart, I had to conduct a mathematical definition for the pattern in this analysis. This definition is determined by these three points:

The wick to the upside has to be smaller than 25% of the size of the candle body.

The wick to the downside has to be bigger than 200% of the size of the candle body.

The range from highest price to lowest price of the candle has to be bigger than 75% of the average range from highest to lowest price of the last 7 candles before in this chart. This rule was set in place to exclude very small hammer candles.

Hanging Man:Textbook candleformation, where thehanging mancandle is a bearishreversal pattern.Hammer:Textbook candleformation, where thehammer candleis a bullish reversalpattern.DEFINITION OF CANDLEHAMMER

Data Sample

After defining the candle formation, we have to collect data to examine, whether the hammer candle acts as a reversal pattern in crypto. Therefore, I collected daily trading data of 19 major cryptocurrencies against USD (BTC, ETH, XRP, BCH, LTC, EOS, BNB, XMR, TRX, XLM, ADA, XTZ, LINK, NEO, ETC, DASH, IOTA, DOGE and BAT). I restricted the data sample to these 19 cryptos, since I only wanted to include assets that had decent trading volumes for a longer period and were listed on multiple exchanges. The result is a data sample with 27,729 records.

Findings

Of these 27,729 records, 402 fit the definition of a hammer/hanging man candle. That is around 1.45%, meaning we saw a hammer/hanging man candle on average every 69 days in a major crypto chart against USD. Comparing the closing price of the candle to daily closing prices after it occurred, we see an average increase of 0.1% after 1 day, 0.52% after 3 days and 2.13% after 7 days (Median: -0.54% after 1 day, -0.69% after 3 days, -0.89% after 7 days). Not surprisingly, the percentage values are not very conclusive, because they contain both a presumed bullish reversal and a presumed bearish reversal.

If we now want to separate hammer and hanging man candle from each other, it gets a little more complicated. A hammer candle only occurs, if there was a downtrend before the candle, while a hanging man only occurs, if there was an uptrend in the days prior to the candle. To define an uptrend/downtrend prior to the candle, we have to define a fixed timespan, in which the trend appeared. We also have to define a minimum value for the size of the trend to exclude tiny movements.

Accordingly, I started with 1-day movements. A hammer candle is identified, if the opening price of the day prior was higher than the hammer candle’s closing price by at least the size of the hammer’s candle body. Subsequently, a hanging man candle is identified, if the opening price of the day prior is lower than the hanging man’s candle by at least the size of the hanging man’s candle body. This 1-day algorithm found 183 hammer candles and 101 hanging man candles. The results are not very promising though: On average, the 1-day hammer candles led to a price increase of 0.47% compared to 1 day later, which barely confirms the bullish reversal pattern. Surprisingly, the 1-day hanging man pattern also led to a price increase, namely 1.35%, compared to the closing price 1 day later. This number shows the exact opposite effect to the bearish reversal that this pattern should have.

As a summary, the 1-day hammer/hanging man candles were not indicative of any trends. Therefore, I tested 3-day and 7-day movements that followed the same calculation than the 1-day movements but with 3-day and 7-day intervals. The algorithm identified 137 3-day hammer candles, 89 3-day hanging man candles, 78 7-day hammer candles and 63 7-day hanging man candles. The results are again different than expected: The 3-day hammer led to an average price decline of 2.03% while the 7-day hammer led to a price decline of 2.63%. The 3-day hanging man led to a price increase of 3.89%, while the 7-day hanging man led to a price increase of 1.9%. In both cases the results contradict the assumed effects.

Conclusion

Although the hammer/hanging man candle is said to be a trend reversal candlestick pattern, the underlying analysis of this article could not prove a correlation between the appearance of the pattern and a trend reversal. The hammer candles that should have signaled a bullish reversal actually led on average to a further price decline, while the hanging man candles, which should have proven a bearish reversal, actually led on average to a further price increase. Concluding from that, trades that were based on the assumed impact of hammer and hanging man candles did not necessarily turn out profitable in the considered data sample. However, the occurrence of both candle patterns has been rare, so that a bigger data sample could provide different results. Nonetheless, the two candle patterns could not be proven to be good predictive indicators for past cryptocurrency price movements, which in my personal opinion signals that they shouldn’t be given too much recognition in a chart prediction.

It was in early 2019, when Binance announced the creation of a native blockchain for their ecosystem. Their goal was to launch a platform that can handle very large loads and is perfectly suited to exchange assets. Today, Binance Chain has been live for nearly 9 months, with block 1 created in April 2019. It has witnessed rapid growth ever since with the onboarding of over 100 assets and close to 50 million transactions. In this article, I will present aggregated statistics on Binance chain transactions and assets.

Transaction Growth

Let’s look at the transaction growth first. In the second quarter of 2019, Binance Chain already had a total of 3.2 million transactions. This number went up significantly higher in Q3 2019, with a total of 20.7 million transactions. It also increased in Q4, when the total number of transactions rose by another 15% to a total of 23.7 million transactions. As a comparison, the Ethereum network had 59.6 million transactions in Q4 2019.

You might ask: How can a new platform like Binance Chain already have a transaction number as high as 40% of Ethereum’s transactions in Q4 2019? Well, Binance Chain has a unique feature, which is on-chain orders. Every time an order is placed or canceled on Binance’s decentralized exchange, it is logged in a transaction on Binance’s blockchain. 95% of the transactions on Binance Chain in 2019 were on-chain orders, which were either “place order” (63%) or “cancel order” (32%). Just around 5% of the total of 47.5 million transactions in 2019 were transfer transactions.

Binance Coin is by far not the only asset on Binance Chain. Since its inception, an increasing number of assets have been issued on Binance Chain. From just 7 assets issued in April 2019, the mark of 100 assets issued was already broken in July 2019. Until the end of 2019, there have been 172 assets issued on Binance Chain. However, some of them were burned completely afterwards and don’t appear in the explorer under the list of assets anymore. That’s possible due to Binance Chain’s native “burn” transaction type, which has already been used 60 times by different coins and tokens in 2019. Talking about listings on Binance DEX, this number expectedly correlates with the number of issued assets, even though it shows, that not all issued assets were listed on the DEX.

22991112121CUMULATIVE NUMBER OF LISTINGS OVER TIME:212335260CUMULATIVE NUMBER OF BURN TRANSACTIONS OVER TIME:Apr.2019Jun.2019Aug.2019Oct.2019Dec.2019784145166172CUMULATIVE NUMBER OF ISSUED ASSETS OVER TIME:GROWTH OF CHAINBINANCE CHAIN

Assets

Let’s take a closer look at the assets on Binance Chain. To measure the adoption of an asset, I like to consider the amount of holders of at least 100 USD of this particular asset. It is better than the total amount of holders, since it ignores addresses with just tiny amounts of a token, whereas an amount of 100 USD is also small enough to not only focus on whales. At the end of 2019, RUNE had the most addresses with a balance of above 100 USD on Binance Chain with 1,056 addresses. It was also the only token with more than 1,000 addresses above a 100 USD balance. Second spot went to CAS with 894 addresses, while BOLT was placed third with 787 addresses. The ranks 4 to 8 belonged to MTXLT, ONE, CHZ, FTM and CRPT and were relatively close with around 650 addresses. On number 9 we can find RAVEN, which had 483 addresses above 100 USD.

However, to not only measure adoption of assets but also usage of the chain, I analyzed the assets that caused the most transactions on Binance Chain as well. Please note that this list excludes the “cancel order” transaction type, since the explorer doesn’t attribute this transaction type to particular assets. With 5.2 million transactions and therefore accountable for 16% of all transactions on Binance Chain, DEFI has the number one spot in this category. It is followed by UND (1.37M transactions), USDSB (1.28M), COS (1.16M), CBM (1.06M), NEW (921K) and ARN (915K). The other assets on Binance Chain combine for 63.4% of all transactions. Interesting side note: The top assets by transaction type differ completely from the top assets by addresses with a balance over 100 USD per asset.

As the native token of the chain, BNB is excluded from the list of transactions by asset, since it is involved in every transaction. I also had to exclude it from the list of addresses with a balance of over 100 USD for a particular asset, since the Binance Chain Explorer doesn’t display more than 1500 addresses in a rich list per asset. However, in a past analysis I did a few months ago, there were around 10,000 addresses holding BNB worth 100 USD or more.

DEFI16%CBM3.3%USDSB3.9%ARN2.8%COS3.6%OTHERS63.4%UND4.2%NEW2.8%PERCENTAGE OF TRANSACTIONS PER ASSET (WITHOUT BNB):RAVEN483CRPT619FTM647CHZ656ONE664MTXLT686BOLT787CAS894RUNE1056ADDRESSES WITH A BALANCE OVER 100 USD PER ASSET:STATISTICS PER ASSETBINANCE CHAIN

Wrap-up

This article discussed several statistics of Binance Chain. Since its launch in Q2 2019, Binance Chain has executed close to 50 million transactions. 95% of those transactions are on-chain orders on Binance DEX, 5% are transfers of assets. Throughout 2019, the number of assets issued on Binance Chain increased to 172. 30 of those assets have over 100 addresses holding 100 USD worth of that asset, while 13 assets combine for 50% of all transactions on Binance Chain. All in all, Binance Chain has been successfully onboarding assets and users in 2019 and it will be exciting to watch its further growth in 2020.